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Published on: December 19, 2020
COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
K Srinivas1, R Gagana Sri1, K Pravallika2
1Department of CSE, VR Siddhartha Engineering College, Vijayawada, 520007 India.
Insights
A new hybrid deep learning model, Inception V3 with VGG16 (IV3-VGG), accurately predicts COVID-19 from chest X-rays. This automated system offers a promising alternative to traditional diagnostic methods, achieving 98% accuracy.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Infectious Diseases
Background:
- Coronavirus disease-2019 (COVID-19) diagnosis relies on methods like Serology, RT-PCR, and Antigen testing, which have limitations including cost and turnaround time.
- Chest X-rays offer a lower-cost imaging alternative to CT scans for COVID-19 screening.
- Existing deep learning models for chest X-ray analysis face challenges like overfitting and misclassification.
Purpose of the Study:
- To develop an automated COVID-19 detection system using chest X-ray images.
- To propose a novel hybrid deep learning model, Inception V3 with VGG16 (IV3-VGG), to overcome limitations of existing methods.
- To evaluate the performance of the IV3-VGG model in predicting COVID-19.
Main Methods:
- A hybrid deep learning model (IV3-VGG) was developed by combining Inception V3 and VGG16 architectures.
- The model incorporates a pre-processing module to handle variations in image size and intensity.
- The IV3-VGG architecture consists of four blocks, with VGG16 in the first and Inception V3 in the subsequent blocks, followed by classification layers.
Main Results:
- The IV3-VGG model achieved a high accuracy of 98% in predicting COVID-19 from chest X-rays.
- The hybrid model demonstrated superior performance compared to individual models like Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.
- The study utilized a dataset of 243 chest X-ray images (121 COVID-19 positive, 122 normal).
Conclusions:
- The proposed IV3-VGG hybrid model is effective for automated COVID-19 prediction using chest X-rays.
- This deep learning approach offers a highly accurate and potentially cost-effective diagnostic tool.
- The IV3-VGG model shows significant potential in improving the efficiency and accuracy of COVID-19 screening.
Abstract:
The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.
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